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Re: st: Methods for selection bias

From   "R.E. De Hoyos" <[email protected]>
To   <[email protected]>
Subject   Re: st: Methods for selection bias
Date   Fri, 21 Apr 2006 19:29:36 +0100

If you are interested in selection bias, you cannot avoid reading Heckman:

Heckman, J. (1979) `Sample selection bias as a
specification error', Econometrica, vol. 47, 1.

Heckman, J. (1990) `Varieties of selection bias',
The American Economic Review, vol. 80, 2.

Carneiro, Hansen and Heckman (2003), `Estimating...', International
Economic Review, May, Vol. 44, No.2


----- Original Message ----- From: "Anders Alexandersson" <[email protected]>
To: <[email protected]>
Sent: Friday, April 21, 2006 2:45 PM
Subject: Re: st: Methods for selection bias

I would consider -permute- for permutation tests based on Monte Carlo
simulations. What assumptions can you make, e.g., do you have
independent samples (i.e. unmatched data)?

Anders Alexandersson
[email protected]

Privately, Hema replied:
The "non-randomised data" refers to both the sample and the treatment assignment.
On 4/20/06, Anders Alexandersson <[email protected]> wrote:
Does "non-randomised data" here refer to the sample and/or to the
treatment assignment?

On 4/20/06, Hema Mistry <[email protected]> wrote:

> I was wondering whether you can provide me with some advice or point me > in the right
> direction. I am trying to find methods which can deal with data that > is non-randomised
> and suffers from selection bias. After searching various databases etc > I have come up
> with the following methods:
> 1) Regression analyses
> 2) Propensity score - matching, stratification, regression, > classification trees
> 3) Instrumental variables
> 4) Sample selection models
> 5) Two-part models
> 6) Inverse probability weighting
> Before I start using these methods in various datasets I was just > wondering whether
> users are aware of any other methods which I have not identified?
> Can you recommend any good text books or key people that maybe I should > contact?
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